Deep Learning-Based Detection of Catenary Support Component Defect and Fault in High-Speed Railways

Zhigang Liu (IEEE Fellow, IET Fellow, AAIA Fellow) received the Ph.D. degree in Power system and its Automation from Southwest Jiaotong University, China in 2003. He is currently a Full Professor of the School of Electrical Engineering, Southwest Jiaotong University, Chengdu. He is also a Guest Professor of Tongji University. Shanghai. He has authored three books and published more than 200 peer-reviewed journal and conference articles. His research interests include the electrical relationship of EMUs and traction, detection, and assessment of pantograph-catenary in high-speed railway. Dr. Liu is an Associate Editor-in-Chief of IEEE Transactions on Instrumentation and Measurement, Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Vehicular Technology and IEEE Access. He received the IEEE TIM's Outstanding Associate Editors for 2019, 2020 and 2021, and the Outstanding Reviewer of IEEE Transactions on Instrumentation and Measurement in 2018.  Wenqiang Liu (IEEE Member) received his Ph.D. degree in electrical engineering from the School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China, in 2021. From 2017 to 2019, he was a joint Ph.D. in the Department of Engineering Structures, Delft University of Technology, Delft, the Netherlands. He is currently a postdoc researcher in the Department of National Rail Transit Electrification and Automation Engineering Technology Research Center, the Hong Kong Polytechnic University, Hong Kong, China. His research interests include artificial intelligence, computer vision, imaging, signal processing, and their applications in fault diagnosis and maintenance of railway infrastructures. Dr. Liu is an associate editor of IEEE Transactions on Instrumentation and Measurement (IEEE TIM). He received the IEEE TIM's Outstanding Editor in 2022 and the Outstanding Reviewer in 2021.   Junping Zhong (IEEE Member) received his Ph.D. degree in electrical engineering from Southwest Jiaotong University, Chengdu, China, in 2022. From Oct 2019 to Oct 2020, he is a Ph.D student visitor in the Department of Railway Engineering, Delft University of Technology, Netherlands. From Feb 2023, he is a Postdoctoral Fellow in the Department of Industrial and Systems Engineering, Hong Kong Polytechnic University. His research interests include image processing, signal processing, and their applications in railway infrastructure fault detection. He has published 11 SCI/EI journal papers and 4 conference papers. He severs as a reviewer for IEEE TITS, IEEE TIM, and Applied Soft Computing. He was selected as the Outstanding Reviewer of IEEE Transactions on Instrumentation and Measurement in 2021.

Overview of Catenary Detection of Electrified Railways.- Advance of Deep Learning.- Catenary Support Components and their Characteristics in High-speed Railways.- Preprocessing of Catenary Support Components’ Images.- Positioning of Catenary Support Components.- Detection of Catenary Support Component Defect and Fault.- Detection of The parameters of Catenary Support Devices based on 3D Point Clouds.